Anomaly detection in business processes using process mining and fuzzy association rule learning

Sarno, Riyanarto; Sinaga, Fernandes; Sungkono, Kelly Rossa

doi:10.1186/s40537-019-0277-1

Cited by 50 publications

(20 citation statements)

References 19 publications

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“…In general, these models are based on variables (also known as predictors) that are most likely to influence the outcome [ 26 ]. Predictive models are widely applied in various applications such as weather forecasting [ 27 , 28 , 29 ], Bayesian spam filters [ 30 , 31 , 32 , 33 ], business [ 34 , 35 , 36 , 37 ], and fraud detection [ 38 , 39 , 40 ]. Predictive models typically include a machine learning algorithm that learns certain properties from a training dataset.…”

Section: Methodsmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

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Section: Methodsmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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“…Although data mining and process mining share many features, the key difference is that data mining aims to discover previously unknown and interesting patterns in the datasets, while process mining focuses on finding process relationships [28]. Thus, data mining techniques for detecting fraud are usually unsuitable for analyzing the behavior of control flow in a business process [39]. However, process mining can be used to assess the control flow of a business process [56] and to analyze process performance, event sequence, and process roles [57].…”

Section: Methodsmentioning

confidence: 99%

“…[7], [30], [31] Process mining anomaly techniques include control flow analysis, role resource analysis, throughput time analysis, and decision point analysis [39]. The study undertaken by [4], which proposed a process mining method for PBF detection, suggested the concept "1 + 5 + 1", which includes (1) log preparation; (5) (a) log analysis, (b) performance analysis, (c) social analysis, (d) conformance analysis, (e) process analysis; and (1) refocusing and iteration.…”

Section: Process Map (Aggregated Data)mentioning

confidence: 99%

Multi-Dimensional Fraud Detection Metrics in Business Processes and their Application

Omair¹,

Alturki²

2020

IJACSA

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Occupational fraud is defined as the deliberate misuse of one's occupation for personal enrichment. It poses a significant challenge for organizations and governments. Estimates indicate that the funds involved in occupational fraud cases investigated across 125 countries between 2018 and 2019 exceeded US$3.6 billion. Process-based fraud (PBF) is a form of occupational fraud that is perpetrated inside business processes. Business processes underlie the logic of the work that organizations undertake, and they are used to execute an organization's strategies to achieve organizational goals. Business processes should be examined for potential fraud risks to ensure that businesses achieve their objectives. While it is impossible to prevent fraud entirely, it must be detected. However, PBF detection metrics are not well developed at present. They are scattered, unstandardized, not validated, and, in some cases, absent. This study aimed to develop a comprehensive PBF detection metric by leveraging and operationalizing a taxonomy of fraud detection metrics for business processes as an underlying theory. 41 PBF detection metrics were deduced from the taxonomy using design science research. To evaluate their utility, the application of the metrics was undertaken using illustrative scenarios, and a real example of the implementation of the metrics was provided. The developed metrics form a complete, classified, validated, and standardized list of PBF detection metrics, which include all the necessary PBF detection dimensions. It is expected that the stakeholders involved in PBF detection will use the metrics established in this work in their practice to increase the effectiveness of the PBF detection process.

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“…Monitoring business processes from a massive event log brings a challenge related to Big Data. Process mining is a discipline of gathering the event log and processing the log into a process model for monitoring, including capturing anomalies [5,6] or bottlenecks [7]. The technique of constructing a process model by process mining is called process discovery.…”

Section: Introductionmentioning

confidence: 99%

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

Sarno

Sungkono

Taufiqulsa’di

et al. 2020

Preprint

Self Cite

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Process discovery helps companies to automatically discover their existing business processes based on the huge, stored event log. The algorithms of process discovery have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by α $ method, is a type of relation that combines the non-free choice and the invisible task. α $ proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of α $ check the combination of invisible task within non-free choice. The checking processes is time consuming, and results in high computing times of α $. This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relations of the graphs. The analysis of the graph relations by rules of GIT is more efficient than the iterative process of checking combined activities by α $. This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Storing a streaming event log in a graph-database has the lowest computing time than storing in other databases, i.e., SQL and MongoDB. Discovering a process model by GIT algorithm has less time complexity than that by α $, wherein GIT obtains O(n3) and α $ obtains O(n4) . In terms of computing time, GIT algorithm is 0.89 faster on batch event log and 0.85 seconds faster on streaming event log than α $. Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

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Anomaly detection in business processes using process mining and fuzzy association rule learning

Cited by 50 publications

References 19 publications

Indirect Recognition of Predefined Human Activities

Indirect Recognition of Predefined Human Activities

Multi-Dimensional Fraud Detection Metrics in Business Processes and their Application

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

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